The use of machine learning in credit allocation should allow lenders to better extend credit, but the shift from traditional to machine learning lending models may have important distributional effects for consumers. This column studies the effect of machine learning on mortgage lending in the US. It finds that machine learning would offer lower rates to racial groups who already were at an advantage under the traditional model, but it would also benefit disadvantaged groups by enabling them to obtain a mortgage in the first place.

After seven successful workshops, the organizing committee of this small informal workshop invites submissions of high-quality theoretical and empirical research on financial intermediation. Scholars in the fields of banking and finance will meet to discuss current issues in banking, financial stability, and financial regulation, focusing on policy reforms for a stable global financial environment. The workshop will provide an opportunity for presentations and discussions about policy-relevant research in an informal and highly interactive environment.

Housing bubbles may crowd out credit from other sectors, but they may also have a crowding-in effect by providing collateral to real estate-owning firms or generating attractive assets which banks can securitise and use to increase their credit supply. This column applies data from the Spanish housing bubble to a simple model of a closed economy to show that both effects were present. At first, the crowding-out effect dominated, but then crowding in occurred. This model can be applied to similar positive shocks in other sectors.